Researchers from 11 European institutions, led by the DTU National Food Institute, have pioneered a novel technique for analyzing wastewater data that could transform public health surveillance. This breakthrough allows for the detection of disease-causing bacteria, viruses, and antimicrobial resistance, pinpointing their sources—whether from humans, animals, industry, or the environment. The method has the potential to identify thousands of threats simultaneously, including antimicrobial resistance and cholera, aiding in the prevention of outbreaks from escalating into epidemics. This groundbreaking research has been published in Nature Communications.

The team analyzed samples collected over three years from wastewater treatment plants in five major European cities: Bologna, Budapest, Copenhagen, Rome, and Rotterdam.

“Untreated wastewater is becoming a critical resource for anonymous health and disease surveillance in large urban populations. However, extracting useful data from it is complex due to the mixture of bacteria from diverse sources, such as humans, animals, and even rainwater,” says Assistant Professor Patrick Munk from the DTU National Food Institute, corresponding author of the research.

Overcoming Complex Challenges with New Tech

The variability of wastewater, influenced by factors like seasonal temperature changes, complicates data extraction. However, the researchers have made significant strides in addressing these challenges using a sophisticated computer program.

“Our research highlights the enormous potential of metagenomics-based wastewater monitoring,” says Professor Frank Aarestrup, leader of the Research Group for Genetic Epidemiology at DTU and co-author of the study. “While this method is more expensive than PCR testing, which was highly effective during the COVID-19 pandemic, PCR only targets one threat at a time. Metagenomics, on the other hand, can detect thousands of threats simultaneously. Moreover, the value of each individual sample grows over time as more historical data becomes available.”

A hybrid monitoring system combining metagenomics with targeted PCR tests could be the future, allowing authorities to monitor specific threats they deem imminent.

This research is particularly timely, as a new EU directive mandates that all major European cities monitor antimicrobial resistance in wastewater. In Denmark, Statens Serum Institut is leading a large European collaboration on implementing this wastewater monitoring.

Software Unveils Hidden Patterns in Wastewater

Over the course of three years, 278 wastewater samples were collected and analyzed at DTU. The team processed billions of DNA sequences, reconstructing genomes from thousands of bacterial species—1,334 of which were previously unknown.

The analysis relied on software developed by the University of Bologna, which sorted species that behaved similarly over time into distinct groups.

“As we analyzed the data, we noticed that bacteria clustered into distinct groups. Initially, we thought the clusters represented microbes collaborating, but that wasn’t the case. Eventually, we discovered that many of the clusters were from human feces, which helped us unlock further insights,” says Munk.

Other groups were linked to environmental bacteria, and one cluster found across all treatment plants likely originated from biofilms on the pipes feeding into the facilities.

Once the researchers began identifying these groups, the analysis became more straightforward. “Certain bacteria always come from humans, and by tracking the sequences that follow them, we can deduce which groups of species are linked to human activity,” Munk explains.

A Major Leap Forward in Data Analysis

The study’s success rate in identifying bacteria represents a significant advancement in wastewater research. Previous studies could only trace about 10% of DNA sequences to specific species, but the new method has increased that number to nearly 70%.

This enhanced detection capability is vital, as it may uncover new sources of antimicrobial resistance. “In this study, we identified over 1,300 previously unknown bacterial species, and the ability to detect these new bacteria could be key to understanding the spread of antimicrobial resistance,” says Munk.

While this observational study provided valuable insights, it didn’t allow the researchers to control variables influencing bacterial frequency. The next step is to create a synthetic dataset where conditions can be manipulated to observe outcomes more precisely.

“We’re still refining the method, but it’s clear we’re on to something significant. We need to continue optimizing the approach to further improve its accuracy,” Munk concludes.